Modelling of input data uncertainty for the financial evaluation of complex infrastructure projects

The planning of complex infrastructure projects represents an interdisciplinary process, which is characterised and heavily influenced by uncertain information and imprecise input parameters. During early planning stages the majority of technical as well as economic parameters, which are of crucial importance for the detailed design and project implementation, cannot be determined with precision. It is therefore common practice that these figures are selected as deterministic values, which require extensive optimisation throughout subsequent planning stages. A major disadvantage inherent to commonly used deterministic analysis is the lack of objectivity for the selection of input parameters. Moreover, it cannot be ensured that the entire existing parameter range and all possible parameter combinations are covered. Probabilistic methods utilise discrete probability distributions or parameter input ranges to cover the entire range of uncertainties resulting from an information deficit during the planning phase and integrate them into the optimisation process by means of alternative calculation methods. In the field of geotechnical engineering this approach has been employed successfully to objectively account for uncertainties related to geological conditions and material properties in the context of design analysis. The article examines to what extent the random set theory (RST) is suitable as a reliable, scientific methodology that can be utilised for handling of vague information and imprecise input parameters in the context of economic project appraisal. The primary applications of RST in this context are the identification, analysis and management of project risks. The method can also be utilised to stipulate and evaluate the decision criteria, which are used to support the process of investment decision making. Furthermore the RST can represent a suitable instrument for the re-evaluation of a project's feasibility under changed technical and economic boundary conditions. This is of particular interest for energy projects such as hydro-power generation, under consideration of the steadily increasing energy prices and a significantly growing demand for renewable energy (compare Pottler [13]).